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Regularization
97 directly classified papers
Papers per year
2007: 2
2008: 2
2009: 2
2010: 3
2011: 2
2012: 2
2013: 6
2014: 3
2016: 4
2017: 3
2018: 2
2019: 8
2020: 14
2021: 9
2022: 12
2023: 11
2024: 9
2025: 3
Papers
Semantic Label Smoothing for Sequence to Sequence Problems
EMNLP 2020
Network as Regularization for Training Deep Neural Networks: Framework, Model and Performance
AAAI 2020
On the training dynamics of deep networks with $L_2$ regularization
NIPS 2020
VECA: A Method for Detecting Overfitting in Neural Networks (Student Abstract)
AAAI 2020
Regularizing Black-box Models for Improved Interpretability
NIPS 2020
Certified Monotonic Neural Networks
NIPS 2020
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
AAAI 2020
A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains
AAAI 2020
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
NIPS 2019
MixUp as Locally Linear Out-of-Manifold Regularization
AAAI 2019
Cost-sensitive Regularization for Label Confusion-aware Event Detection
ACL 2019
On Implicit Filter Level Sparsity in Convolutional Neural Networks
CVPR 2019
Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters
AAAI 2019
Guided Dropout
AAAI 2019
Using Benson’s Algorithm for Regularization Parameter Tracking
AAAI 2019
Regularized Weighted Low Rank Approximation
NIPS 2019
Dropout Training, Data-dependent Regularization, and Generalization Bounds
ICML 2018
Avoiding Speaker Overfitting in End-to-End DNNs Using Raw Waveform for Text-Independent Speaker Verification
INTERSPEECH 2018
Early stopping for kernel boosting algorithms: A general analysis with localized complexities
NIPS 2017
Information-theoretic analysis of generalization capability of learning algorithms
NIPS 2017
On Optimal Generalizability in Parametric Learning
NIPS 2017
Split LBI: An Iterative Regularization Path with Structural Sparsity
NIPS 2016
Adaptive Lasso and group-Lasso for functional Poisson regression
JMLR 2016
On Regularizing Rademacher Observation Losses
NIPS 2016
Recurrent Dropout without Memory Loss
COLING 2016
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